Seattle, Washington; Arlington, Virginia; North Charleston, South Carolina; Berkeley, Missouri
Job ID JR2025468279 Category Data Science & Analytics Role Type Onsite Post Date Aug. 26, 2025

JOB DESCRIPTION

At Boeing, we innovate and collaborate to make the world a better place. We’re committed to fostering an environment for every teammate that’s welcoming, respectful and inclusive, with great opportunity for professional growth. Find your future with us.
The Boeing Company’s Talent Analytics and Innovations team is currently seeking a Principal Data Solutions Architect to enhance excellence within our People Analytics organization. This role can be based in Seattle, WA, North Charleston, SC, Berkeley, MO or Arlington, VA.
This senior, hands-on role involves designing and managing critical data platforms while mentoring and developing data talent. You will set technical direction, define standards and best practices, and collaborate with cross-functional teams to deliver reliable, scalable, and secure people-data solutions.
An ideal candidate will have a proven track record of driving best practices and building high-performing, collaborative data teams, along with the ability to balance pragmatic delivery with long-term architectural health and governance.

BASIC QUALIFICATIONS (REQUIRED SKILLS/EXPERIENCE):

• 10+ years in in senior/principle technical role such as data or software architecture
• 5+ years of programming experience
• Experience delivering production-grade data platforms and pipelines
• Experience mentoring and elevating peers, leading cross-team technical initiatives, and influence culture
• Experience with Teradata (or similar), Hadoop, Spark, Kafka, and both batch and streaming processing patterns
• Experience with cloud data platforms such as AWS, GCP, or Azure and cloud-native data services
• Experience with SQL, relational, NoSQL and graph databases and query languages
• Experience in designing data warehouses, data lakes, dimensional models, CDC/event-driven architectures, and high-throughput pipeline designs
• Experience using orchestration tools such as containerization, Kubernetes, IaC (Terraform), CI/CD, automated testing, and observability tooling
• Experience explaining complex technical topics clearly and mentoring non-technical stakeholders

PREFERRED QUALIFICATIONS (DESIRED SKILLS/EXPERIENCE):

• Experience with people/HR data, HRIS integrations, and handling confidential data
• Experience with metadata/catalog tools (Amundsen, DataHub, Collibra)
• Experience with Machine Learning (ML) workflows/MLOps supporting people analytics models
• Experience leading technical transformations or platform consolidations

• Lead architecture and implementation of scalable extract, transform, load (ETL/ELT) and streaming pipelines for people data
• Define long-term roadmap and migration strategies
• Establish and evangelize standards, patterns, and reusable components for data pipelines, storage, and access
• Drive code quality through design reviews, automated testing standards, continuous integration/continuous delivery (CI/CD) pipelines, and observability practices
• Implement performance, cost, and reliability targets
• Own runbooks, Service Level Agreements (SLAs), and incident remediation strategies
• Mentor and coach data engineers on design, coding, testing, operations, and career growth
• Run regular technical training, pairing, and knowledge-sharing sessions
• Support hiring, interview calibration, and onboarding to grow the team’s capability and culture
• Partner with data architects, data scientists, Human Resources (HR) business partners, and Information Technology (IT)/security to translate business needs into robust data solutions and ensure operational readiness
• Communicate trade-offs, technical decisions, and risks to technical and non-technical stakeholders
• Lead adoption of modern data management technologies and cloud-native patterns
• Design data warehouse/lake strategies and integration approaches
• Standardize metadata, lineage, and governance integrations to improve discoverability and compliance
• Champion data governance and privacy best practices for sensitive HR data
• Enforce access controls, encryption, and anonymization where appropriate
• Ensure thorough documentation of architectures, Application Programming Interfaces (APIs), schemas, runbooks, and best practices to promote maintainability and compliance